About This Architecture

Multi-stage LLM orchestration pipeline on Azure combines vector retrieval, deterministic engines for technician allocation and cost calculation, and confidence scoring to generate validated outputs. User requests flow from a web app through preprocessing, vector retrieval, context injection into an LLM generation layer, then through validation and governance checks before structured output. Confidence scoring gates results to human review when certainty thresholds aren't met, ensuring production reliability for business-critical AI applications. Fork this architecture on Diagrams.so to customize layers, swap Azure OpenAI for other LLM providers, or integrate your own deterministic business logic. Ideal for teams building trustworthy AI systems where accuracy and auditability matter more than speed alone.

People also ask

How do I architect an LLM pipeline on Azure with human review for low-confidence predictions?

This Azure architecture routes user requests through preprocessing, vector retrieval, deterministic engines for allocation and costing, LLM generation with context injection, validation layers, and confidence scoring that gates outputs to human review when certainty is insufficient, ensuring production reliability.